This survey has examined several sota open-domain conversational ai models.
Despite their noticeable successes and the general progress, challenges still remain.
The challenges contribute to the non-human-like utterances the conversational ai tend to have.
These challenges also provide motivation for active research in nlp.
For example, the basic seq2seq architecture is known for repetitive and dull responses [1]}.
One way of augmenting the architecture for refined responses is the use of ir techniques, like concatenation of retrieved sentences from Wikipedia to the conversation context [2]}.
Other shortcomings may be handled by switching the objective function to a mutual information objective or introducing the beam search decoding algorithm in order to achieve relatively more diverse responses [1]}.
Besides, gpt-3 is observed to lose coherence over really long passages, gives contradictory utterances, and its size is so large that it's difficult to deploy.
Collectively, some of the existing challenges are highlighted below.
It is hoped that identifying these challenges will spur further research in these areas.
